ASIC Engineer, Performance Architecture and Modeling (University Grad)

Meta's Silicon Engineering organization is building custom silicon solutions that power the infrastructure underpinning Meta's AI and data center workloads at scale. As an ASIC Engineer specializing in architecture, performance and modeling, you will define and drive the architectural performance analysis, pre-silicon modeling, and microarchitectural exploration of custom ASICs designed for Meta's infrastructure. In this role, you will establish functional and performance modeling methodologies and long-term silicon roadmap strategy, as well as develop and analyze workloads, partnering with architecture, design, and software teams to ensure Meta's infrastructure silicon meets the demanding throughput, latency, and efficiency targets required at hyperscale.

Responsibilities

Drive microarchitectural exploration and trade-off analysis across compute, memory subsystem, interconnect, and I/O domains to inform silicon architecture decisions Define and own the performance modeling strategy for custom infrastructure ASICs, including development of cycle-accurate and transaction-level simulation environments Develop and maintain C++ models of AI chip IPs and subsystems for architecture exploration, performance analysis, and software development Develop low-level workloads and kernels for machine learning training and inference applications Establish performance analysis methodologies, benchmarking frameworks, and bottleneck identification techniques across the full ASIC pipeline Collaborate with architecture, RTL design, and physical design teams to translate performance requirements into implementable microarchitectural specifications Partner with software and systems teams to co-optimize workload scheduling, firmware, and driver behavior against silicon performance characteristics Lead cross-functional technical reviews and communicate performance modeling results and architectural recommendations to engineering leadership Define long-term performance modeling infrastructure roadmaps, including tooling, automation, and simulation platform investments Mentor other engineers on performance modeling techniques, simulation methodology, and microarchitectural analysis best practices Identify and resolve performance gaps between modeled and measured silicon behavior through structured root-cause analysis and model calibration

Qualifications

Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta Currently has, or is in the process of obtaining a Master's or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta Proficiency in C++ and Python for developing simulation models, automation frameworks, and performance analysis tools Experience in ASIC performance modeling, microarchitectural analysis, or pre-silicon simulation for custom silicon or SoC designs Experience with the performance analysis of data center, AI accelerator, or high-performance computing workloads on custom silicon Experience developing cycle-accurate or transaction-level performance models using C++ and SystemC for complex digital systems, including processors, memory subsystems, or high-speed interconnects Experience defining microarchitectural specifications and driving cross-functional alignment across architecture, RTL, and physical design teams Experience with assembly programming languages, and with compiler technologies Experience developing Python-based automation pipelines for simulation orchestration, regression testing, and performance data analysis Experience with AI numerics, data types, math functions, and precision/accuracy analysis Experience building or scaling performance modeling infrastructure for hyperscale data center ASICs, including network, storage, or AI inference accelerator designs Experience writing and optimizing compute/collective kernels in CUDA or equivalent GPU programming frameworks Familiarity with post-silicon performance validation and model-to-hardware correlation methodologies Experience with hardware description languages (e.g., SystemVerilog, VHDL) and simulation environments used in ASIC development flows Experience with ML domain knowledge including GenAI inference and Attention mechanisms Experience with high-level synthesis, power-performance-area trade-off analysis, or PPA-driven microarchitectural optimization